Abstract：To overcome the disadvantages of food source updating and the mechanism for onlooker bees to select food source in the Artificial Bee Colony (ABC) algorithm,an ABC algorithm based on tracking search and immune selection is proposed. The search methods for tracking the global optimal solution and randomly selecting solution are introduced on the basis of the original solution searching method. The searched optimal solution is selected as the candidate in order to accelerate the convergence of the population and improve the convergence of the algorithm. For the procedure of the onlooker bees selecting the food source,the regulation mechanism of antibody density in the immune system is introduced to keep the diversity of the population and enhance the global search ability of the traditional algorithm. The simulation results for 6 classical benchmark functions show that the improved algorithm has obvious advantages in the optimization accuracy and convergence rate compared with the original ABC,GABC,RABC and TABC.
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2013, Vol. 26 
